System and method for large-scale lane marking detection using multimodal sensor data

ABSTRACT

A system and method for large-scale lane marking detection using multimodal sensor data are disclosed. A particular embodiment includes: receiving image data from an image generating device mounted on a vehicle; receiving point cloud data from a distance and intensity measuring device mounted on the vehicle; fusing the image data and the point cloud data to produce a set of lane marking points in three-dimensional (3D) space that correlate to the image data and the point cloud data; and generating a lane marking map from the set of lane marking points.

PRIORITY/RELATED DOCUMENTS

This patent document claims the benefit of U.S. patent application Ser.No. 15/822,689, filed on Nov. 27, 2017, which is incorporated herein byreference in its entirety for all purposes.

COPYRIGHT NOTICE

A portion of the disclosure of this patent document contains materialthat is subject to copyright protection. The copyright owner has noobjection to the facsimile reproduction by anyone of the patent documentor the patent disclosure, as it appears in the U.S. Patent and TrademarkOffice patent files or records, but otherwise reserves all copyrightrights whatsoever. The following notice applies to the disclosure hereinand to the drawings that form a part of this document: Copyright2019-2020, TuSimple Inc, All Rights Reserved.

TECHNICAL FIELD

This patent document pertains generally to tools (systems, apparatuses,methodologies, computer program products, etc.) for image processing,vehicle control systems, vehicle navigation, and autonomous drivingsystems, and more particularly, but not by way of limitation, to asystem and method for large-scale lane marking detection usingmultimodal sensor data.

BACKGROUND

The detection of lane markings is a prerequisite for many driverassistance systems as well as for autonomous vehicles. Lane markingsseparate roads from the non-drivable environment and provide informationabout the position and direction of the lanes and roadways. Detection oflane marking using visual information from cameras is typically used inmany conventional lane detection systems. Usually, a camera is mountedon the front of the vehicle to capture the road images. However, thecaptured images may be of poor quality in various environments, weatherconditions, lighting conditions, and the like. Moreover, the lanemarkings may vary in color (white and yellow for India, USA, and Europe;blue for South Korea), width, continuity, and shape (solid and dashed).As such, conventional lane detection systems cannot perform well in manyreal-world driving environments.

BRIEF DESCRIPTION OF THE DRAWINGS

The various embodiments are illustrated by way of example, and not byway of limitation, in the figures of the accompanying drawings in which:

FIG. 1 illustrates a block diagram of an example ecosystem in which amultimodal lane detection module of an example embodiment can beimplemented;

FIGS. 2 and 3 illustrate the components of the multimodal lane detectionmodule of an example embodiment;

FIGS. 4 and 5 illustrate sample images of roadway lane markingsprocessed by an example embodiment;

FIG. 6 is a process flow diagram illustrating an example embodiment of asystem and method for large-scale lane marking detection usingmultimodal sensor data; and

FIG. 7 shows a diagrammatic representation of machine in the exampleform of a computer system within which a set of instructions whenexecuted may cause the machine to perform any one or more of themethodologies discussed herein.

DETAILED DESCRIPTION

In the following description, for purposes of explanation, numerousspecific details are set forth in order to provide a thoroughunderstanding of the various embodiments. It will be evident, however,to one of ordinary skill in the art that the various embodiments may bepracticed without these specific details.

As described in various example embodiments, a system and method forlarge-scale lane marking detection using multimodal sensor data aredisclosed herein. In various example embodiments described herein, alane marking detection system and method fuses multimodal sensor datafrom different types of sensor devices (e.g., cameras, laser lightdetection and ranging [LIDAR] devices, and the like) and producesaccurate lane markings in a lane marking map. By leveraging theadvantages of different types of sensors, the various exampleembodiments described herein can detect lane markings on roads forhundreds of miles with centimeter-level accuracy in conditions thatwould cause conventional systems to fail.

Some traditional systems focus on utilizing only single modality sensordata (e.g., either camera or LIDAR, but not both). The camera-basedapproaches rely on image processing or deep learning to detect lanemarkings in the image space. Empowered by deep learning, these methodscan learn features of lanes quite well and they are robust to differenttypes of lane markings. However, subject to the optical limitations ofthe cameras, image-based methods are susceptible to over-exposure,occlusion, small view angles, and inaccurate two dimensional (2D) tothree dimensional (3D) transformations. On the other hand, LIDAR-basedmethods use 3D positions and intensity as features to detect lanemarkings. The point clouds captured by LIDAR are inherently in the 3Dspace and therefore are more accurate in terms of 3D positions. However,LIDAR point clouds are vulnerable to noise or roadside objects that havehigh reflection intensity, such as rubbles and stones.

The challenges of lane marking detection come from three main aspects:

-   -   1) Sensor limitations—Both LIDAR and cameras have their        advantages, but could fail under some circumstances. The lane        detection system and method must find a proper way to fuse the        information from different sensors to mitigate the inability of        each type of sensor to reliably provide robust lane marking        detection.    -   2) Sensor calibration—Sensor calibration, such as sensor        coordinate transformation or time alignment, are important for        accurate lane detection. The lane detection system and method        will provide a better result only when the noise introduced        during the calibration stage is kept below a certain level.    -   3) Continuity and smoothness—Highway maps that support        autonomous driving must be continuous and smooth, even when the        lane marking length could sometimes reach up to hundreds of        miles. In this situation, the system must be able to deal with        ramps, merges, and exits to guarantee the continuity of the        detected lane markings.

The various example embodiments described herein use multimodal sensordata to produce accurate and reliable lane marking detection. Theexample embodiments also can produce among the following features andbenefits as well:

-   -   1) A fusion of multimodal sensor data to generate a lane marking        map that utilizes the advantages of multiple types of sensors.    -   2) Introduction of a sub-pixel linearly decreasing function from        close to far to address the perspective projection problem. In        one example embodiment, more LIDAR projected points far away are        given the same distance to the fitted lines to accommodate        perspective projection.    -   2) The systems and methods described herein can detect solid or        dotted lane markings, and also non-traditional lane separators,        such as cat's eyes or reflectors.    -   4) The various example embodiments described herein are        configured to obtain an adaptive threshold for the lane markings        from image based road segmentation. As a result, the various        example embodiments can learn to detect lane markings even        through different types of road surfaces, weather conditions,        lighting conditions, construction zones, and the like.

These and other features and benefits of the systems and methods forlarge-scale lane marking detection using multimodal sensor data invarious example embodiments are described in more detail below.

Referring now to FIG. 1 , various example embodiments disclosed hereincan be used in the context of a control system 150 in a vehicleecosystem 101. In one example embodiment, a control system 150 with amultimodal lane detection module 200 resident in a vehicle 105 can beconfigured like the architecture and ecosystem 101 illustrated in FIG. 1. However, it will be apparent to those of ordinary skill in the artthat the multimodal lane detection module 200 described and claimedherein can be implemented, configured, and used in a variety of otherapplications and systems as well.

Referring again to FIG. 1 , a block diagram illustrates an exampleecosystem 101 in which a control system 150 and a multimodal lanedetection module 200 of an example embodiment can be implemented. Thesecomponents are described in more detail below. Ecosystem 101 includes avariety of systems and components that can generate and/or deliver oneor more sources of information/data and related services to the controlsystem 150 and the multimodal lane detection module 200, which can beinstalled in the vehicle 105. For example, a camera installed in thevehicle 105, as one of the devices of vehicle subsystems 140, cangenerate image and timing data that can be received by the controlsystem 150. The control system 150 and the multimodal lane detectionmodule 200 executing thereon can receive this image and timing datainput. As described in more detail below, the multimodal lane detectionmodule 200 can process the input image data, process the input LIDARpoint clouds, process the input vehicle metric data (e.g., vehicleposition, speed, heading etc.), perform lane detection using themultimodal sensor data, as described in more detail below, and produce acorresponding lane marking map. The results of the processing can beused to accurately detect lane boundaries proximate to the location ofthe autonomous vehicle 105. The lane boundary information can be used byan autonomous vehicle control subsystem, as another one of thesubsystems of vehicle subsystems 140. The autonomous vehicle controlsubsystem, for example, can use the lane boundary information to safelyand efficiently control the vehicle 105 in a real world or simulateddriving scenario while avoiding obstacles and safely controlling thevehicle.

In an example embodiment as described herein, the control system 150 canbe in data communication with a plurality of vehicle subsystems 140, allof which can be resident in a user's vehicle 105. A vehicle subsysteminterface 141 is provided to facilitate data communication between thecontrol system 150 and the plurality of vehicle subsystems 140. Thecontrol system 150 can be configured to include a data processor 171 toexecute the multimodal lane detection module 200 for processing imagedata, LIDAR data, and vehicle metric data received from one or more ofthe vehicle subsystems 140. The data processor 171 can be combined witha data storage device 172 as part of a computing system 170 in thecontrol system 150. The data storage device 172 can be used to storedata, processing parameters, and data processing instructions. Aprocessing module interface 165 can be provided to facilitate datacommunications between the data processor 171 and the multimodal lanedetection module 200. In various example embodiments, a plurality ofprocessing modules, configured similarly to multimodal lane detectionmodule 200, can be provided for execution by data processor 171. Asshown by the dashed lines in FIG. 1 , the multimodal lane detectionmodule 200 can be integrated into the control system 150 or optionallydownloaded to the control system 150.

The control system 150 can be configured to receive or transmit datafrom/to a wide-area network 120 and network resources 122 connectedthereto. A web-enabled device 130 and/or a user mobile device 132 can beused to communicate via network 120. A web-enabled device interface 131can be used by the control system 150 to facilitate data communicationbetween the control system 150 and the network 120 via the web-enableddevice 130. Similarly, a user mobile device interface 133 can be used bythe control system 150 to facilitate data communication between thecontrol system 150 and the network 120 via the user mobile device 132.In this manner, the control system 150 can obtain real-time access tonetwork resources 122 via network 120. The network resources 122 can beused to obtain processing modules for execution by data processor 171,data content to train internal neural networks, system parameters, orother data.

The ecosystem 101 can include a wide area data network 120. The network120 represents one or more conventional wide area data networks, such asthe Internet, a cellular telephone network, satellite network, pagernetwork, a wireless broadcast network, gaming network, WiFi network,peer-to-peer network, Voice over IP (VoIP) network, etc. One or more ofthese networks 120 can be used to connect a user or client system withnetwork resources 122, such as websites, servers, central control sites,or the like. The network resources 122 can generate and/or distributedata, which can be received in vehicle 105 via web-enabled devices 130or user mobile devices 132. The network resources 122 can also hostnetwork cloud services, which can support the functionality used tocompute or assist in processing image input or image input analysis.Antennas can serve to connect the control system 150 and the multimodallane detection module 200 with the data network 120 via cellular,satellite, radio, or other conventional signal reception mechanisms.Such cellular data networks are currently available (e.g., Verizon™AT&T™, T-Mobile™, etc.). Such satellite-based data or content networksare also currently available (e.g., SiriusXM™, HughesNet™, etc.). Theconventional broadcast networks, such as AM/FM radio networks, pagernetworks, UHF networks, gaming networks, WiFi networks, peer-to-peernetworks, Voice over IP (VoIP) networks, and the like are alsowell-known. Thus, as described in more detail below, the control system150 and the multimodal lane detection module 200 can receive web-baseddata or content via web-enabled device interface 131, which can be usedto connect with the web-enabled device receiver 130 and network 120. Inthis manner, the control system 150 and the multimodal lane detectionmodule 200 can support a variety of network-connectable devices andsystems from within a vehicle 105.

As shown in FIG. 1 , the control system 150 and the multimodal lanedetection module 200 can also receive data, image processing or LIDARdata processing control parameters, and training content from usermobile devices 132, which can be located inside or proximately to thevehicle 105. The user mobile devices 132 can represent standard mobiledevices, such as cellular phones, smartphones, personal digitalassistants (PDA's), MP3 players, tablet computing devices (e.g., iPad™),laptop computers, CD players, and other mobile devices, which canproduce, receive, and/or deliver data, image processing controlparameters, and content for the control system 150 and the multimodallane detection module 200. As shown in FIG. 1 , the mobile devices 132can also be in data communication with the network cloud 120. The mobiledevices 132 can source data and content from internal memory componentsof the mobile devices 132 themselves or from network resources 122 vianetwork 120. Additionally, mobile devices 132 can themselves include aglobal positioning system (GPS) data receiver, accelerometers, WiFitriangulation, or other geo-location sensors or components in the mobiledevice, which can be used to determine the real-time geo-location of theuser (via the mobile device) at any moment in time. In any case, thecontrol system 150 and the multimodal lane detection module 200 canreceive data from the mobile devices 132 as shown in FIG. 1 .

Referring still to FIG. 1 , the example embodiment of ecosystem 101 caninclude vehicle operational subsystems 140. For embodiments that areimplemented in a vehicle 105, many standard vehicles include operationalsubsystems, such as electronic control units (ECUs), supportingmonitoring/control subsystems for the engine, brakes, transmission,electrical system, emissions system, interior environment, and the like.For example, data signals communicated from the vehicle operationalsubsystems 140 (e.g., ECUs of the vehicle 105) to the control system 150via vehicle subsystem interface 141 may include information or vehiclemetrics related to the state of one or more of the components orsubsystems of the vehicle 105. In particular, the data signals, whichcan be communicated from the vehicle operational subsystems 140 to aController Area Network (CAN) bus of the vehicle 105, can be receivedand processed by the control system 150 via vehicle subsystem interface141. Embodiments of the systems and methods described herein can be usedwith substantially any mechanized system that uses a CAN bus or similardata communications bus as defined herein, including, but not limitedto, industrial equipment, boats, trucks, machinery, or automobiles;thus, the term “vehicle” as used herein can include any such mechanizedsystems. Embodiments of the systems and methods described herein canalso be used with any systems employing some form of network datacommunications; however, such network communications are not required.

Referring still to FIG. 1 , the example embodiment of ecosystem 101, andthe vehicle operational subsystems 140 therein, can include a variety ofvehicle subsystems in support of the operation of vehicle 105. Ingeneral, the vehicle 105 may take the form of a car, truck, motorcycle,bus, boat, airplane, helicopter, lawn mower, earth mover, snowmobile,aircraft, recreational vehicle, amusement park vehicle, farm equipment,construction equipment, tram, golf cart, train, and trolley, forexample. Other vehicles are possible as well. The vehicle 105 may beconfigured to operate fully or partially in an autonomous mode. Forexample, the vehicle 105 may control itself while in the autonomousmode, and may be operable to determine a current state of the vehicleand its environment, determine a predicted behavior of at least oneother vehicle in the environment, determine a confidence level that maycorrespond to a likelihood of the at least one other vehicle to performthe predicted behavior, and control the vehicle 105 based on thedetermined information. While in autonomous mode, the vehicle 105 may beconfigured to operate without human interaction.

The vehicle 105 may include various vehicle subsystems such as a vehicledrive subsystem 142, vehicle sensor subsystem 144, vehicle controlsubsystem 146, and occupant interface subsystem 148. As described above,the vehicle 105 may also include the control system 150, the computingsystem 170, and the multimodal lane detection module 200. The vehicle105 may include more or fewer subsystems and each subsystem couldinclude multiple elements. Further, each of the subsystems and elementsof vehicle 105 could be interconnected. Thus, one or more of thedescribed functions of the vehicle 105 may be divided up into additionalfunctional or physical components or combined into fewer functional orphysical components. In some further examples, additional functional andphysical components may be added to the examples illustrated by FIG. 1 .

The vehicle drive subsystem 142 may include components operable toprovide powered motion for the vehicle 105. In an example embodiment,the vehicle drive subsystem 142 may include an engine or motor,wheels/tires, a transmission, an electrical subsystem, and a powersource. The engine or motor may be any combination of an internalcombustion engine, an electric motor, steam engine, fuel cell engine,propane engine, or other types of engines or motors. In some exampleembodiments, the engine may be configured to convert a power source intomechanical energy. In some example embodiments, the vehicle drivesubsystem 142 may include multiple types of engines or motors. Forinstance, a gas-electric hybrid car could include a gasoline engine andan electric motor. Other examples are possible.

The wheels of the vehicle 105 may be standard tires. The wheels of thevehicle 105 may be configured in various formats, including a unicycle,bicycle, tricycle, or a four-wheel format, such as on a car or a truck,for example. Other wheel geometries are possible, such as thoseincluding six or more wheels. Any combination of the wheels of vehicle105 may be operable to rotate differentially with respect to otherwheels. The wheels may represent at least one wheel that is fixedlyattached to the transmission and at least one tire coupled to a rim ofthe wheel that could make contact with the driving surface. The wheelsmay include a combination of metal and rubber, or another combination ofmaterials. The transmission may include elements that are operable totransmit mechanical power from the engine to the wheels. For thispurpose, the transmission could include a gearbox, a clutch, adifferential, and drive shafts. The transmission may include otherelements as well. The drive shafts may include one or more axles thatcould be coupled to one or more wheels. The electrical system mayinclude elements that are operable to transfer and control electricalsignals in the vehicle 105. These electrical signals can be used toactivate lights, servos, electrical motors, and other electricallydriven or controlled devices of the vehicle 105. The power source mayrepresent a source of energy that may, in full or in part, power theengine or motor. That is, the engine or motor could be configured toconvert the power source into mechanical energy. Examples of powersources include gasoline, diesel, other petroleum-based fuels, propane,other compressed gas-based fuels, ethanol, fuel cell, solar panels,batteries, and other sources of electrical power. The power source couldadditionally or alternatively include any combination of fuel tanks,batteries, capacitors, or flywheels. The power source may also provideenergy for other subsystems of the vehicle 105.

The vehicle sensor subsystem 144 may include a number of sensorsconfigured to sense information or vehicle metrics related to anenvironment or condition of the vehicle 105. For example, the vehiclesensor subsystem 144 may include an inertial measurement unit (IMU), aGlobal Positioning System (GPS) transceiver, a Radar unit, a laser rangefinder/LIDAR unit (or other distance and intensity measuring device),and one or more cameras or image capturing devices. The vehicle sensorsubsystem 144 may also include sensors configured to monitor internalsystems of the vehicle 105 (e.g., an O² monitor, a fuel gauge, an engineoil temperature, etc.). Other sensors are possible as well. One or moreof the sensors included in the vehicle sensor subsystem 144 may beconfigured to be actuated separately or collectively in order to modifya position, an orientation, or both, of the one or more sensors.

The IMU may include any combination of sensors (e.g., accelerometers andgyroscopes) configured to sense position and orientation changes of thevehicle 105 based on inertial acceleration. The GPS transceiver may beany sensor configured to estimate a geographic location of the vehicle105. For this purpose, the GPS transceiver may include areceiver/transmitter operable to provide information regarding theposition of the vehicle 105 with respect to the Earth. The Radar unitmay represent a system that utilizes radio signals to sense objectswithin the local environment of the vehicle 105. In some embodiments, inaddition to sensing the objects, the Radar unit may additionally beconfigured to sense the speed and the heading of the objects proximateto the vehicle 105. The laser range finder or LIDAR unit (or otherdistance measuring device) may be any sensor configured to sense objectsin the environment in which the vehicle 105 is located using lasers. Inan example embodiment, the laser range finder/LIDAR unit may include oneor more laser sources, a laser scanner, and one or more detectors, amongother system components. The laser range finder/LIDAR unit could beconfigured to operate in a coherent (e.g., using heterodyne detection)or an incoherent detection mode. The laser range finder/LIDAR unit istypically configured to produce point clouds representing measureddistances at various points in three dimensional (3D) space in front ofor adjacent to a vehicle on which the laser range finder/LIDAR unit ismounted. The cameras or image capturing devices may include one or moredevices configured to capture a plurality of images of the environmentof the vehicle 105. The cameras may be still image cameras or motionvideo cameras.

The vehicle control system 146 may be configured to control operation ofthe vehicle 105 and its components. Accordingly, the vehicle controlsystem 146 may include various elements such as a steering unit, athrottle, a brake unit, a navigation unit, and an autonomous controlunit. The steering unit may represent any combination of mechanisms thatmay be operable to adjust the heading of vehicle 105. The throttle maybe configured to control, for instance, the operating speed of theengine and, in turn, control the speed of the vehicle 105. The brakeunit can include any combination of mechanisms configured to deceleratethe vehicle 105. The brake unit can use friction to slow the wheels in astandard manner. In other embodiments, the brake unit may convert thekinetic energy of the wheels to electric current. The brake unit maytake other forms as well. The navigation unit may be any systemconfigured to determine a driving path or route for the vehicle 105. Thenavigation unit may additionally be configured to update the drivingpath dynamically while the vehicle 105 is in operation. In someembodiments, the navigation unit may be configured to incorporate datafrom the multimodal lane detection module 200, the GPS transceiver, andone or more predetermined maps so as to determine the driving path forthe vehicle 105. The autonomous control unit may represent a controlsystem configured to identify, evaluate, and avoid or otherwisenegotiate potential obstacles in the environment of the vehicle 105. Ingeneral, the autonomous control unit may be configured to control thevehicle 105 for operation without a driver or to provide driverassistance in controlling the vehicle 105. In some embodiments, theautonomous control unit may be configured to incorporate data from themultimodal lane detection module 200, the GPS transceiver, the Radar,the LIDAR, the cameras, and other vehicle subsystems to determine thedriving path or trajectory for the vehicle 105. The vehicle controlsystem 146 may additionally or alternatively include components otherthan those shown and described.

Occupant interface subsystems 148 may be configured to allow interactionbetween the vehicle 105 and external sensors, other vehicles, othercomputer systems, and/or an occupant or user of vehicle 105. Forexample, the occupant interface subsystems 148 may include standardvisual display devices (e.g., plasma displays, liquid crystal displays(LCDs), touchscreen displays, heads-up displays, or the like), speakersor other audio output devices, microphones or other audio input devices,navigation interfaces, and interfaces for controlling the internalenvironment (e.g., temperature, fan, etc.) of the vehicle 105.

In an example embodiment, the occupant interface subsystems 148 mayprovide, for instance, means for a user/occupant of the vehicle 105 tointeract with the other vehicle subsystems. The visual display devicesmay provide information to a user of the vehicle 105. The user interfacedevices can also be operable to accept input from the user via atouchscreen. The touchscreen may be configured to sense at least one ofa position and a movement of a user's finger via capacitive sensing,resistance sensing, or a surface acoustic wave process, among otherpossibilities. The touchscreen may be capable of sensing finger movementin a direction parallel or planar to the touchscreen surface, in adirection normal to the touchscreen surface, or both, and may also becapable of sensing a level of pressure applied to the touchscreensurface. The touchscreen may be formed of one or more translucent ortransparent insulating layers and one or more translucent or transparentconducting layers. The touchscreen may take other forms as well.

In other instances, the occupant interface subsystems 148 may providemeans for the vehicle 105 to communicate with devices within itsenvironment. The microphone may be configured to receive audio (e.g., avoice command or other audio input) from a user of the vehicle 105.Similarly, the speakers may be configured to output audio to a user ofthe vehicle 105. In one example embodiment, the occupant interfacesubsystems 148 may be configured to wirelessly communicate with one ormore devices directly or via a communication network. For example, awireless communication system could use 3G cellular communication, suchas CDMA, EVDO, GSM/GPRS, or 4G cellular communication, such as WiMAX orLTE. Alternatively, the wireless communication system may communicatewith a wireless local area network (WLAN), for example, using WIFI®. Insome embodiments, the wireless communication system 146 may communicatedirectly with a device, for example, using an infrared link, BLUETOOTH®,or ZIGBEE®. Other wireless protocols, such as various vehicularcommunication systems, are possible within the context of thedisclosure. For example, the wireless communication system may includeone or more dedicated short range communications (DSRC) devices that mayinclude public or private data communications between vehicles and/orroadside stations.

Many or all of the functions of the vehicle 105 can be controlled by thecomputing system 170. The computing system 170 may include at least onedata processor 171 (which can include at least one microprocessor) thatexecutes processing instructions stored in a non-transitory computerreadable medium, such as the data storage device 172. The computingsystem 170 may also represent a plurality of computing devices that mayserve to control individual components or subsystems of the vehicle 105in a distributed fashion. In some embodiments, the data storage device172 may contain processing instructions (e.g., program logic) executableby the data processor 171 to perform various functions of the vehicle105, including those described herein in connection with the drawings.The data storage device 172 may contain additional instructions as well,including instructions to transmit data to, receive data from, interactwith, or control one or more of the vehicle drive subsystem 142, thevehicle sensor subsystem 144, the vehicle control subsystem 146, and theoccupant interface subsystems 148.

In addition to the processing instructions, the data storage device 172may store data such as image processing parameters, machine learningtraining data, semantic label image data, LIDAR point cloud data, laneboundary information, roadway maps, and path information, among otherinformation. Such information may be used by the vehicle 105 and thecomputing system 170 during the operation of the vehicle 105 in theautonomous, semi-autonomous, and/or manual modes.

The vehicle 105 may include a user interface for providing informationto or receiving input from a user or occupant of the vehicle 105. Theuser interface may control or enable control of the content and thelayout of interactive images that may be displayed on a display device.Further, the user interface may include one or more input/output deviceswithin the set of occupant interface subsystems 148, such as the displaydevice, the speakers, the microphones, or a wireless communicationsystem.

The computing system 170 may control the function of the vehicle 105based on inputs received from various vehicle subsystems (e.g., thevehicle drive subsystem 142, the vehicle sensor subsystem 144, and thevehicle control subsystem 146), as well as from the occupant interfacesubsystem 148. For example, the computing system 170 may use input fromthe vehicle control system 146 in order to control the steering unit toavoid an obstacle detected by the vehicle sensor subsystem 144 and themultimodal lane detection module 200. In an example embodiment, thecomputing system 170 can be operable to provide control over manyaspects of the vehicle 105 and its subsystems.

Although FIG. 1 shows various components of vehicle 105, e.g., vehiclesubsystems 140, computing system 170, data storage device 172, controlsystem 150, and multimodal lane detection module 200, as beingintegrated into the vehicle 105, one or more of these components couldbe mounted or associated separately from the vehicle 105. For example,data storage device 172 could, in part or in full, exist separately fromthe vehicle 105. Thus, the vehicle 105 could be provided in the form ofdevice elements that may be located separately or together. The deviceelements that make up vehicle 105 could be communicatively coupledtogether in a wired or wireless fashion. In various example embodiments,the control system 150 and the multimodal lane detection module 200 indata communication therewith can be implemented as integrated componentsor as separate components. In an example embodiment, the softwarecomponents of the control system 150 and/or the multimodal lanedetection module 200 can be dynamically upgraded, modified, and/oraugmented by use of the data connection with the mobile devices 132and/or the network resources 122 via network 120. The control system 150can periodically query a mobile device 132 or a network resource 122 forupdates or updates can be pushed to the control system 150.

In an example embodiment, the multimodal lane detection module 200 canbe configured to include an interface with the control system 150, asshown in FIG. 1 , through which the multimodal lane detection module 200can send and receive data as described herein. Additionally, themultimodal lane detection module 200 can be configured to include aninterface with the control system 150 and/or other ecosystem 101subsystems through which the multimodal lane detection module 200 canreceive ancillary data from the various data sources described above.The ancillary data can be used to augment, modify, or train theoperation of the multimodal lane detection module 200 based on a varietyof factors including, the context in which the user is operating thevehicle (e.g., the location of the vehicle, the specified destination,direction of travel, speed, the time of day, the status of the vehicle,etc.), and a variety of other data obtainable from the variety ofsources, local and remote, as described herein. As described above, themultimodal lane detection module 200 can also be implemented in systemsand platforms that are not deployed in a vehicle and not necessarilyused in or with a vehicle.

System and Method for Large-Scale Lane Marking Detection UsingMultimodal Sensor Data

Various example embodiments disclosed herein describe a system andmethod for large-scale lane marking detection using multimodal sensordata. In particular, example embodiments provide systems and methodssupporting advanced driver assistance systems or autonomous drivingsystems to generate accurate lane marking detection with multimodalsensor data by using image analysis supported by a convolutional neuralnetwork, LIDAR point cloud data processing, and vehicle metrics from thevehicle GPS and IMU subsystems. Example embodiments are described inmore detail below.

Referring now to FIGS. 2 and 3 , the components of an example embodimentare illustrated. The components shown in FIGS. 2 and 3 can beimplemented as software modules or components of the control system 150and/or the multimodal lane detection module 200. It will be apparent tothose of ordinary skill in the art in view of the disclosure herein thatthe illustrated components can be implemented as integrated componentsor as separate components. Each of these modules can be implemented assoftware, firmware, or other logic components executing or activatedwithin an executable environment of the multimodal lane detection module200 operating within or in data communication with the control system150. Each of these modules of an example embodiment is described in moredetail below in connection with the figures provided herein.

Referring to FIG. 2 , a data collector 210 can be configured to gathermultimodal sensor data 212 from a plurality of different types ofsensors (e.g., cameras or image capture devices, LIDAR devices, andvehicle sensor subsystems 144, such as the GPS and IMU subsystems). Inan example embodiment, the sensor modules, both LIDAR and camera, can bemounted on the top or front of the vehicle 105 while the camera ispositioned to face the front of the vehicle. The images or image dataand timing data from the cameras can be obtained by the data collector210 and processed for time and location alignment with the point clouddata received from the LIDAR. The GPS and IMU data received with thevehicle metrics can be used to determine the location, orientation, andspeed of the vehicle and thereby enable the correlation and timealignment of the image data with the point cloud data. In an exampleembodiment, the image data and the point cloud data can be transformedto a common coordinate space—the image data in a 2D image space and thepoint cloud data in a 3D space. The aligned image data can be stored ina data storage module 215 for use by the other processing modules of themultimodal lane detection module 200 shown in FIG. 3 . The other modulescan use the data storage module 215 as an input source for the alignedimage data. The point cloud data obtained by the data collector 210 fromthe LIDAR can be passed to a point cloud accumulator 220, which canregister the point cloud data within a time range to the commoncoordinate space and generate an accumulated LIDAR point cloudrepresenting a collection of aligned point cloud data over time. Thepoint cloud accumulator 220 can also use the GPS and IMU data todetermine the location, orientation, and speed of the vehicle andthereby enable the correlation and time alignment of the accumulatedpoint cloud data. The accumulated and aligned point cloud data can beretained in the point cloud accumulator 220 for use by the otherprocessing modules of the multimodal lane detection module 200 shown inFIG. 3 . The other modules can use the point cloud accumulator 220 as aninput source for the accumulated and aligned point cloud data.

Referring now to FIG. 3 , other processing modules of the multimodallane detection module 200 are shown. As described above, the datastorage module 215 provides an input source for the aligned image dataand the point cloud accumulator 220 provides an input source for theaccumulated and aligned point cloud data. As shown in FIG. 3 , themultimodal lane detection module 200 includes a deep image analysismodule 225 for processing the aligned image data received from thecameras via the data collector 210 and the data storage module 215. Thedeep image analysis module 225 can be configured to process the receivedimage data and apply image segmentation using a deep convolutionalneural network 227. Image semantic segmentation is used to identify theimage regions corresponding directly to objects in an image by labelingeach pixel in the image with a semantic category. As such, semanticsegmentation assigns a category label to each pixel to indicate anobject type to which the pixel belongs. In the context of lane markingdetection, the deep convolutional neural network 227 can be trained torecognize and label pixels of an input image that are likely related toa lane marker.

Prior to real-time operational use, the deep convolutional neuralnetwork 227 can be trained to produce the desired output for a giveninput. In an example embodiment, there are two main objectives duringthe neural network training phase. Firstly, the neural network shouldlearn to categorize each pixel into the correct class or category, whichis performed by the semantic segmentation operation of the deepconvolutional neural network 227. Secondly, the neural network shouldlearn to particularly categorize each pixel into classes correspondingto lane marking objects. To configure or train the neural network inthis manner, training images of lane markings in a wide variety ofcontexts, environments, locations, weather conditions, lightingconditions, and the like are used in an offline training process toconfigure parameters of the neural network. The training images caninclude corresponding object labeling created manually by human labelersor automated processes. By use of the labeled training images, theneural network can be trained to categorize each pixel of an input imageinto classes or categories corresponding to lane marking objects, whenappropriate. In an example embodiment, the segmentation process canfirst distinguish lane marking objects from the background pixels of aninput image. Secondly, the segmentation process can consider only thepixels recognized as lane marking objects and then distinguishparticular instances of lane objects from each other.

Referring now to FIG. 4 , three related image samples show an example ofa roadway with lane markings. In the image portion on the left side ofFIG. 4 , the raw image received from a vehicle camera is shown. In thecenter image portion in the middle of FIG. 4 , the raw image receivedfrom the vehicle camera is shown after a semantic segmentation processhas identified and labeled objects in the image. In this example,roadway lane and boundary markings have been identified and highlighted.In the image portion on the right side of FIG. 4 , the background imageelements have been removed leaving only the highlighted roadway lane andboundary markings. These image samples illustrate the sequence ofoperations performed in an example embodiment for processing an inputimage to identify and isolate image objects related to roadway lane andboundary markings.

Referring again to FIG. 3 , the deep image analysis module 225 canreceive aligned image data from the data storage module 215 and use thetrained deep convolutional neural network 227 to produce lane markingobject data and road segmentation data as segmented image data. Thesegmented image data can include labeled lane marking objects, which canbe represented in a 2D image space. An example of this representation ina 2D image space is shown in the right image portion of FIG. 4 . Theroad segmentation data can correspond to the portions of the image thatcan be categorized as likely road surface. Referring still to FIG. 3 ,the segmented image data produced by the deep image analysis module 225can be provided as an input to the lane fitting module 230. The lanefitting module 230 can be configured to fit a piecewise line for eachlane marking object detected in the segmented image data. The outputproduced by the lane fitting module 230 can be a segmented imageconsisting of a plurality of piecewise lines indicating, for example,the left boundary marking of the driving lane, the right boundarymarking of the driving lane, the left boundary marking of theneighboring left lane, and right boundary marking of the neighboringright lane. An example of this representation in the 2D image space isshown in FIG. 5 . The segmented image data including the plurality ofpiecewise lines for the lane markings and corresponding road image datacan be provided as an input to an image/LIDAR fusion module 250 as shownin FIG. 3 . In some embodiments, the segmented image data includinglabeled lane marking objects as produced by the deep image analysismodule 225 can be provided to the image/LIDAR fusion module 250.Additionally, the point cloud accumulator 220 can provide theaccumulated point cloud data, as described above, as an input to theimage/LIDAR fusion module 250. The image/LIDAR fusion module 250 canalso pull data retained in the data storage module 215. The processingperformed by the image/LIDAR fusion module 250 is described in detailnext.

Although the analysis of images with semantic segmentation is veryeffective and adaptive for providing lane marking labels for each pixelof the input images, the category labels cannot always differentiatebetween different instances of objects with visual characteristicssimilar to lane markings. For example, some objects in the 2D imagespace may be mistakenly labeled (or mistakenly not labeled) as roadwaylane or boundary markings. As described above, camera limitations,adverse weather or lighting conditions, sensor calibration problems, andthe like can conspire to cause the image analysis processing alone toproduce erroneous results. As a solution to this problem, the variousembodiments disclosed herein provide a fusion of the processed imagedata with accumulated LIDAR point cloud data to substantially improvethe lane marking detection data produced by the multimodal lanedetection module 200. This fusion of the processed image data withaccumulated LIDAR point cloud data occurs in the image/LIDAR fusionmodule 250 as shown in FIG. 3 .

As described above, the segmented image data including the plurality ofpiecewise lines for the lane markings and corresponding road image datacan be provided as an input to the image/LIDAR fusion module 250. Thisimage data is provided as a representation in a 2D image space. Thepoint cloud accumulator 220 can provide the accumulated LIDAR pointcloud data, as described above, as another input to the image/LIDARfusion module 250. The accumulated point cloud data is provided as arepresentation in a 3D space. Vehicle metrics and other sensor data canalso be provided by the data storage module 215 to the image/LIDARfusion module 250. As such, the image/LIDAR fusion module 250 receivesmultimodal sensor data from a plurality of different types of sensordevices. In an example embodiment, the processing performed by theimage/LIDAR fusion module 250 can be comprised of the processingoperations described below:

-   -   1) As described above, the segmented image data including the        plurality of piecewise lines for the lane markings and        corresponding road image data can be provided as an input to the        image/LIDAR fusion module 250. The vehicle metrics can also be        provided as inputs to the image/LIDAR fusion module 250. As        such, the GPS and IMU data from the vehicle metrics can be used        to align and orient the segmented image data with a terrain map        corresponding to the geographical location where the host        vehicle is located and from where the images of the segmented        image were taken. Terrain maps are well-known to those of        ordinary skill in the art. As also well-known, terrain maps can        include elevation information as well as feature information        associated with a latitude/longitude. The terrain map can        provide a grid representation wherein each cell of the grid        represents the elevation of the road surface at the        latitude/longitude position corresponding to the cell. Thus,        terrain maps can provide a 3D representation of the geographical        location where the host vehicle is located and from where the        images of the segmented image were taken. Given the segmented        image data, which can be aligned and oriented with the terrain        map, the 2D segmented image data can be back-projected onto the        terrain map thereby enabling a transformation of the 2D        segmented image data to a 3D space with the terrain map        elevation data. Because the segmented image data is aligned and        oriented with the terrain map, the plurality of piecewise lines        for the lane markings and corresponding road image data from the        segmented image data can be associated with a location and an        elevation in 3D space.    -   2) The 3D segmented image data, generated as described above and        including the plurality of piecewise lines for the lane        markings, can be used to identify candidate LIDAR points of the        accumulated 3D LIDAR point cloud data that may be associated        with a lane marking in 3D space. In particular, the image/LIDAR        fusion module 250 processes each LIDAR point to determine if the        distance between the position of the LIDAR point in 3D space and        the position of at least one of the piecewise lines is smaller        (e.g., not greater than or equal to) than a pre-determined        threshold. If the LIDAR point is within the pre-determined        threshold of at least one of the piecewise lines corresponding        to a lane marking in 3D space, the LIDAR point is considered a        candidate associated with that lane marking. Because of        perspective projection, the projected points farther away (at a        greater distance from the camera) are expected to require a        linearly decreasing distance from the one or more of the        piecewise lines and still be considered a candidate LIDAR point.        To solve the unbalanced problem produced by the perspective        projection in an example embodiment, the pre-determined        threshold can be implemented as a linearly decreasing decimal        value as a function of the distance from the camera in 3D space.        As such, the pre-determined threshold will be larger for points        close to the camera position and smaller for points more distant        from the camera position.    -   3) Having determined a set of candidate lane marking LIDAR        points in the previous processing operation, the LIDAR points of        the accumulated 3D LIDAR point cloud can be separately marked as        road surface candidates based on the road segmentation data        produced by the deep image analysis module 225. As described        above, the road segmentation data can be transformed to 3D space        using the terrain map. The 3D LIDAR points marked as road        surface candidates correspond to points labeled as road surface        pixels during the image segmentation process described above.    -   4) At this point, the image/LIDAR fusion module 250 has used the        multimodal sensor data to identify a set of candidate lane        marking LIDAR points and a set of candidate road surface LIDAR        points in 3D space. Given the standard characteristics of LIDAR        point data, each LIDAR point has an associated intensity. In        this processing operation, the image/LIDAR fusion module 250        fits a Gaussian distribution N (m, σ) for the road surface        intensity using the set of candidate road surface LIDAR points.        Given the Gaussian distribution N (m, σ) for the road surface        intensity, the image/LIDAR fusion module 250 can set an        intensity threshold th for the set of candidate lane marking        LIDAR points as a function of the road surface intensity. In one        embodiment, the intensity threshold for the set of candidate        lane marking LIDAR points can be set to th=m+3σ. It will be        apparent to those of ordinary skill in the art in view of the        disclosure herein that the intensity threshold th for the set of        candidate lane marking LIDAR points can be set to other values.        In general, it would be expected that a valid lane marking LIDAR        point would have an intensity value greater than the road        surface intensity.    -   5) Having determined the intensity threshold for the set of        candidate lane marking LIDAR points, the image/LIDAR fusion        module 250 can sum the total number of points in the set of        candidate lane marking LIDAR points identified in step 2 set        forth above. This total number of candidate lane marking LIDAR        points can be denoted as n. Additionally, image/LIDAR fusion        module 250 can determine the total number of points in the set        of candidate lane marking LIDAR points for which the intensity        of the point is greater than the threshold th. This total number        of candidate lane marking LIDAR points with an intensity greater        than the threshold th can be denoted as n1.    -   6) The image/LIDAR fusion module 250 can determine if the        proportion of the total number of candidate lane marking LIDAR        points with an intensity greater than the threshold th relative        to the total number of candidate lane marking LIDAR points is        greater than a pre-determined threshold. In an example        embodiment, the image/LIDAR fusion module 250 can compute the        result of n1/n. If this result is greater than the        pre-determined threshold, the n1 candidate lane marking LIDAR        points with an intensity greater than the threshold th are        retained. In this case, a sufficient quantity of the candidate        lane marking LIDAR points with a sufficient intensity have been        detected and associated with the corresponding lane marking        objects identified in the processed images. If there is not a        sufficient quantity of the candidate lane marking LIDAR points        with a sufficient intensity, all of the n candidate lane marking        LIDAR points are retained; because, the lane marking detected        from the image and LIDAR data analysis may originate from        special lane separators, such as cat's eyes or reflectors, which        may not produce an expected level of intensity from the LIDAR        sensor.    -   7) Once the candidate lane marking LIDAR points are retained as        described above, the image/LIDAR fusion module 250 can pass the        retained lane marking LIDAR points to the post-processor module        255 as described below. Because the retained points are LIDAR        point data and LIDAR point data is inherently three dimensional,        the lane marking with LIDAR points can be represented and        labeled in 3D space. Thus, the image/LIDAR fusion module 250 can        produce lane marking data in 3D space, which cannot be        accomplished using 2D image data alone.

The post-processor module 255 can use the lane marking LIDAR points in3D space produced by the image/LIDAR fusion module 250 to produce a lanemarking map. In particular, the post-processor module 255 can take thelane marking detection results (e.g., the set of retained 3D LIDARpoints indicating the lane markings) produced by the image/LIDAR fusionmodule 250 from each frame of the input images and track the same lanemarkings across consecutive image frames or a plurality of image frames.After associating the same lane markings across a plurality of imageframes, the post-processor can use smoothing techniques (e.g., ab-spline method) to fit smooth new curves for each lane marking acrossmultiple image frames. The smoothed curves can then be sampled togenerate a high-resolution lane marking map. The lane marking map can beoutput by the post-processor module 255 and provided to other vehiclesubsystems for subsequent processing.

As such, the multimodal lane detection module 200 of the exampleembodiment can produce a lane marking map that can be used todistinguish particular lane or roadway boundary markings. Once the lanemarkings in the input images and point cloud data are identified,particular inferences can be determined from the presence and locationof the lane markings and appropriate vehicle control actions can beinitiated.

Referring now to FIG. 6 , a flow diagram illustrates an exampleembodiment of a system and method 1000 for using multimodal sensor datafor lane detection. The example embodiment can be configured for:receiving image data from an image generating device mounted on avehicle (processing block 1010); receiving point cloud data from adistance and intensity measuring device mounted on the vehicle(processing block 1020); fusing the image data and the point cloud datato produce a set of lane marking points in three-dimensional (3D) spacethat correlate to the image data and the point cloud data (processingblock 1030); and generating a lane marking map from the set of lanemarking points (processing block 1040).

As used herein and unless specified otherwise, the term “mobile device”includes any computing or communications device that can communicatewith the control system 150 and/or the multimodal lane detection module200 described herein to obtain read or write access to data signals,messages, or content communicated via any mode of data communications.In many cases, the mobile device 130 is a handheld, portable device,such as a smart phone, mobile phone, cellular telephone, tabletcomputer, laptop computer, display pager, radio frequency (RF) device,infrared (IR) device, global positioning device (GPS), Personal DigitalAssistants (PDA), handheld computers, wearable computer, portable gameconsole, other mobile communication and/or computing device, or anintegrated device combining one or more of the preceding devices, andthe like. Additionally, the mobile device 130 can be a computing device,personal computer (PC), multiprocessor system, microprocessor-based orprogrammable consumer electronic device, network PC, diagnosticsequipment, a system operated by a vehicle 119 manufacturer or servicetechnician, and the like, and is not limited to portable devices. Themobile device 130 can receive and process data in any of a variety ofdata formats. The data format may include or be configured to operatewith any programming format, protocol, or language including, but notlimited to, JavaScript, C++, iOS, Android, etc.

As used herein and unless specified otherwise, the term “networkresource” includes any device, system, or service that can communicatewith the control system 150 and/or the multimodal lane detection module200 described herein to obtain read or write access to data signals,messages, or content communicated via any mode of inter-process ornetworked data communications. In many cases, the network resource 122is a data network accessible computing platform, including client orserver computers, websites, mobile devices, peer-to-peer (P2P) networknodes, and the like. Additionally, the network resource 122 can be a webappliance, a network router, switch, bridge, gateway, diagnosticsequipment, a system operated by a vehicle 119 manufacturer or servicetechnician, or any machine capable of executing a set of instructions(sequential or otherwise) that specify actions to be taken by thatmachine. Further, while only a single machine is illustrated, the term“machine” can also be taken to include any collection of machines thatindividually or jointly execute a set (or multiple sets) of instructionsto perform any one or more of the methodologies discussed herein. Thenetwork resources 122 may include any of a variety of providers orprocessors of network transportable digital content. Typically, the fileformat that is employed is Extensible Markup Language (XML), however,the various embodiments are not so limited, and other file formats maybe used. For example, data formats other than Hypertext Markup Language(HTML)/XML or formats other than open/standard data formats can besupported by various embodiments. Any electronic file format, such asPortable Document Format (PDF), audio (e.g., Motion Picture ExpertsGroup Audio Layer 3-MP3, and the like), video (e.g., MP4, and the like),and any proprietary interchange format defined by specific content sitescan be supported by the various embodiments described herein.

The wide area data network 120 (also denoted the network cloud) usedwith the network resources 122 can be configured to couple one computingor communication device with another computing or communication device.The network may be enabled to employ any form of computer readable dataor media for communicating information from one electronic device toanother. The network 120 can include the Internet in addition to otherwide area networks (WANs), cellular telephone networks, metro-areanetworks, local area networks (LANs), other packet-switched networks,circuit-switched networks, direct data connections, such as through auniversal serial bus (USB) or Ethernet port, other forms ofcomputer-readable media, or any combination thereof. The network 120 caninclude the Internet in addition to other wide area networks (WANs),cellular telephone networks, satellite networks, over-the-air broadcastnetworks, AM/FM radio networks, pager networks, UHF networks, otherbroadcast networks, gaming networks, WiFi networks, peer-to-peernetworks, Voice Over IP (VoIP) networks, metro-area networks, local areanetworks (LANs), other packet-switched networks, circuit-switchednetworks, direct data connections, such as through a universal serialbus (USB) or Ethernet port, other forms of computer-readable media, orany combination thereof. On an interconnected set of networks, includingthose based on differing architectures and protocols, a router orgateway can act as a link between networks, enabling messages to be sentbetween computing devices on different networks. Also, communicationlinks within networks can typically include twisted wire pair cabling,USB, Firewire, Ethernet, or coaxial cable, while communication linksbetween networks may utilize analog or digital telephone lines, full orfractional dedicated digital lines including T1, T2, T3, and T4,Integrated Services Digital Networks (ISDNs), Digital User Lines (DSLs),wireless links including satellite links, cellular telephone links, orother communication links known to those of ordinary skill in the art.Furthermore, remote computers and other related electronic devices canbe remotely connected to the network via a modem and temporary telephonelink.

The network 120 may further include any of a variety of wirelesssub-networks that may further overlay stand-alone ad-hoc networks, andthe like, to provide an infrastructure-oriented connection. Suchsub-networks may include mesh networks, Wireless LAN (WLAN) networks,cellular networks, and the like. The network may also include anautonomous system of terminals, gateways, routers, and the likeconnected by wireless radio links or wireless transceivers. Theseconnectors may be configured to move freely and randomly and organizethemselves arbitrarily, such that the topology of the network may changerapidly. The network 120 may further employ one or more of a pluralityof standard wireless and/or cellular protocols or access technologiesincluding those set forth herein in connection with network interface712 and network 714 described in the figures herewith.

In a particular embodiment, a mobile device 132 and/or a networkresource 122 may act as a client device enabling a user to access anduse the control system 150 and/or the multimodal lane detection module200 to interact with one or more components of a vehicle subsystem.These client devices 132 or 122 may include virtually any computingdevice that is configured to send and receive information over anetwork, such as network 120 as described herein. Such client devicesmay include mobile devices, such as cellular telephones, smart phones,tablet computers, display pagers, radio frequency (RF) devices, infrared(IR) devices, global positioning devices (GPS), Personal DigitalAssistants (PDAs), handheld computers, wearable computers, gameconsoles, integrated devices combining one or more of the precedingdevices, and the like. The client devices may also include othercomputing devices, such as personal computers (PCs), multiprocessorsystems, microprocessor-based or programmable consumer electronics,network PC's, and the like. As such, client devices may range widely interms of capabilities and features. For example, a client deviceconfigured as a cell phone may have a numeric keypad and a few lines ofmonochrome LCD display on which only text may be displayed. In anotherexample, a web-enabled client device may have a touch sensitive screen,a stylus, and a color LCD display screen in which both text and graphicsmay be displayed. Moreover, the web-enabled client device may include abrowser application enabled to receive and to send wireless applicationprotocol messages (WAP), and/or wired application messages, and thelike. In one embodiment, the browser application is enabled to employHyperText Markup Language (HTML), Dynamic HTML, Handheld Device MarkupLanguage (HDML), Wireless Markup Language (WML), WMLScript, JavaScript™,EXtensible HTML (xHTML), Compact HTML (CHTML), and the like, to displayand send a message with relevant information.

The client devices may also include at least one client application thatis configured to receive content or messages from another computingdevice via a network transmission. The client application may include acapability to provide and receive textual content, graphical content,video content, audio content, alerts, messages, notifications, and thelike. Moreover, the client devices may be further configured tocommunicate and/or receive a message, such as through a Short MessageService (SMS), direct messaging (e.g., Twitter), email, MultimediaMessage Service (MMS), instant messaging (IM), internet relay chat(IRC), mIRC, Jabber, Enhanced Messaging Service (EMS), text messaging,Smart Messaging, Over the Air (OTA) messaging, or the like, betweenanother computing device, and the like. The client devices may alsoinclude a wireless application device on which a client application isconfigured to enable a user of the device to send and receiveinformation to/from network resources wirelessly via the network.

The control system 150 and/or the multimodal lane detection module 200can be implemented using systems that enhance the security of theexecution environment, thereby improving security and reducing thepossibility that the control system 150 and/or the multimodal lanedetection module 200 and the related services could be compromised byviruses or malware. For example, the control system 150 and/or themultimodal lane detection module 200 can be implemented using a TrustedExecution Environment, which can ensure that sensitive data is stored,processed, and communicated in a secure way.

FIG. 7 shows a diagrammatic representation of a machine in the exampleform of a computing system 700 within which a set of instructions whenexecuted and/or processing logic when activated may cause the machine toperform any one or more of the methodologies described and/or claimedherein. In alternative embodiments, the machine operates as a standalonedevice or may be connected (e.g., networked) to other machines. In anetworked deployment, the machine may operate in the capacity of aserver or a client machine in server-client network environment, or as apeer machine in a peer-to-peer (or distributed) network environment. Themachine may be a personal computer (PC), a laptop computer, a tabletcomputing system, a Personal Digital Assistant (PDA), a cellulartelephone, a smartphone, a web appliance, a set-top box (STB), a networkrouter, switch or bridge, or any machine capable of executing a set ofinstructions (sequential or otherwise) or activating processing logicthat specify actions to be taken by that machine. Further, while only asingle machine is illustrated, the term “machine” can also be taken toinclude any collection of machines that individually or jointly executea set (or multiple sets) of instructions or processing logic to performany one or more of the methodologies described and/or claimed herein.

The example computing system 700 can include a data processor 702 (e.g.,a System-on-a-Chip (SoC), general processing core, graphics core, andoptionally other processing logic) and a memory 704, which cancommunicate with each other via a bus or other data transfer system 706.The mobile computing and/or communication system 700 may further includevarious input/output (I/O) devices and/or interfaces 710, such as atouchscreen display, an audio jack, a voice interface, and optionally anetwork interface 712. In an example embodiment, the network interface712 can include one or more radio transceivers configured forcompatibility with any one or more standard wireless and/or cellularprotocols or access technologies (e.g., 2nd (2G), 2.5, 3rd (3G), 4th(4G) generation, and future generation radio access for cellularsystems, Global System for Mobile communication (GSM), General PacketRadio Services (GPRS), Enhanced Data GSM Environment (EDGE), WidebandCode Division Multiple Access (WCDMA), LTE, CDMA2000, WLAN, WirelessRouter (WR) mesh, and the like). Network interface 712 may also beconfigured for use with various other wired and/or wirelesscommunication protocols, including TCP/IP, UDP, SIP, SMS, RTP, WAP,CDMA, TDMA, UMTS, UWB, WiFi, WiMax, Bluetooth©, IEEE 802.11x, and thelike. In essence, network interface 712 may include or support virtuallyany wired and/or wireless communication and data processing mechanismsby which information/data may travel between a computing system 700 andanother computing or communication system via network 714.

The memory 704 can represent a machine-readable medium on which isstored one or more sets of instructions, software, firmware, or otherprocessing logic (e.g., logic 708) embodying any one or more of themethodologies or functions described and/or claimed herein. The logic708, or a portion thereof, may also reside, completely or at leastpartially within the processor 702 during execution thereof by themobile computing and/or communication system 700. As such, the memory704 and the processor 702 may also constitute machine-readable media.The logic 708, or a portion thereof, may also be configured asprocessing logic or logic, at least a portion of which is partiallyimplemented in hardware. The logic 708, or a portion thereof, mayfurther be transmitted or received over a network 714 via the networkinterface 712. While the machine-readable medium of an exampleembodiment can be a single medium, the term “machine-readable medium”should be taken to include a single non-transitory medium or multiplenon-transitory media (e.g., a centralized or distributed database,and/or associated caches and computing systems) that store the one ormore sets of instructions. The term “machine-readable medium” can alsobe taken to include any non-transitory medium that is capable ofstoring, encoding or carrying a set of instructions for execution by themachine and that cause the machine to perform any one or more of themethodologies of the various embodiments, or that is capable of storing,encoding or carrying data structures utilized by or associated with sucha set of instructions. The term “machine-readable medium” canaccordingly be taken to include, but not be limited to, solid-statememories, optical media, and magnetic media.

The Abstract of the Disclosure is provided to allow the reader toquickly ascertain the nature of the technical disclosure. It issubmitted with the understanding that it will not be used to interpretor limit the scope or meaning of the claims. In addition, in theforegoing Detailed Description, it can be seen that various features aregrouped together in a single embodiment for the purpose of streamliningthe disclosure. This method of disclosure is not to be interpreted asreflecting an intention that the claimed embodiments require morefeatures than are expressly recited in each claim. Rather, as thefollowing claims reflect, inventive subject matter lies in less than allfeatures of a single disclosed embodiment. Thus, the following claimsare hereby incorporated into the Detailed Description, with each claimstanding on its own as a separate embodiment.

What is claimed is:
 1. A system comprising: a data processor; and amultimodal lane detection module, executable by the data processor, themultimodal lane detection module being configured to: receive image datafrom an image generating device mounted on a vehicle; fit piecewiselines for each lane marking object detected in the received image data;receive point cloud data from a distance and intensity measuring devicemounted on the vehicle; fuse the image data and the point cloud data toproduce a set of lane marking points in three-dimensional (3D) spacethat correlate to the image data and the point cloud data based on athreshold, the threshold being linearly decreased according to aperspective depth of the lane marking points, the threshold being largerfor lane marking points close to a position of the image generatingdevice and smaller for lane marking points more distant from theposition of the image generating device; and generate a lane marking mapfrom the set of lane marking points.
 2. The system of claim 1 wherein aneural network is used for identifying and labeling objects in the imagedata with object category labels on a per-pixel basis.
 3. The system ofclaim 2 wherein the neural network is configured to categorize each ofpixels of the image data into classes corresponding to each of the lanemarking object.
 4. The system of claim 1 wherein the image generatingdevice includes an image camera or a motion video camera.
 5. The systemof claim 1 wherein the distance and intensity measuring device includesa laser range finder.
 6. The system of claim 1 being configured toreceive vehicle metrics related to an environment or a condition of thevehicle from a vehicle subsystem.
 7. The system of claim 1 wherein thefusion including aligning and orienting the image data with a terrainmap corresponding to a location and using a terrain map elevation datato transform the image data to the 3D space, wherein the location is ageographical location where the vehicle is located.
 8. The system ofclaim 1 wherein lane markings formed from lane marking points areproduced from each frame of the received image data and thecorresponding point cloud data.
 9. A method comprising: receiving imagedata from an image generating device mounted on a vehicle; fittingpiecewise lines for each lane marking object detected in the receivedimage data; receiving point cloud data from a distance and intensitymeasuring device mounted on the vehicle; fusing the image data and thepoint cloud data to produce a set of lane marking points inthree-dimensional (3D) space that correlate to the image data and thepoint cloud data based on a threshold, the threshold being linearlydecreased according to a perspective depth of the lane marking points,the threshold being larger for lane marking points close to a positionof the image generating device and smaller for lane marking points moredistant from the position of the image generating device; and generatinga lane marking map from the set of lane marking points.
 10. The methodof claim 9 including tracking lane markings formed from the lane markingpoints across a plurality of frames of the received image data.
 11. Themethod of claim 10 including associating the same lane markings acrossthe plurality of frames to form the lane marking map.
 12. The method ofclaim 11 wherein a smoothing technique is used to fit smooth new curvesfor each lane marking across the plurality of frames.
 13. The method ofclaim 9 wherein the fusing includes projecting 3D point cloud data on totwo-dimensional (2D) image data, and adding a 3D point cloud point tothe set of lane marking points if a distance between a position of theprojected 3D point cloud point in 2D space and a position of at leastone of the piecewise lines is within a pre-determined threshold.
 14. Themethod of claim 9 including receiving vehicle metrics via a GlobalPositioning System (GPS), an inertial measurement unit (IMU), or aradar, to determine at least one of a location, an orientation, or aspeed of the vehicle.
 15. The method of claim 9 including registeringthe point cloud data within a time range to a common coordinate space.16. The method of claim 15 including generating an accumulated pointcloud representing a collection of the point cloud data over time,wherein the point cloud data are aligned.
 17. A non-transitorymachine-useable storage medium embodying instructions which, whenexecuted by a machine, cause the machine to: receive image data from animage generating device mounted on a vehicle; fit piecewise lines foreach lane marking object detected in the received image data; receivepoint cloud data from a distance and intensity measuring device mountedon the vehicle; fuse the image data and the point cloud data to producea set of lane marking points in three-dimensional (3D) space thatcorrelate to the image data and the point cloud data based on athreshold, the threshold being linearly decreased according to aperspective depth of the lane marking points, the threshold being largerfor lane marking points close to a position of the image generatingdevice and smaller for lane marking points more distant from theposition of the image generating device; and generate a lane marking mapfrom the set of lane marking points.
 18. The non-transitorymachine-useable storage medium of claim 17 wherein a neural network isused for identifying and labeling objects in the image data with objectcategory labels.
 19. The non-transitory machine-useable storage mediumof claim 18 wherein the neural network is trained by using trainingimages, wherein the training images include contexts of at least one ofenvironments, locations, weather conditions, and lighting conditions.20. The non-transitory machine-useable storage medium of claim 19wherein the training images include an object labeling created manuallyor automated processes.